The only approved systemic therapy for patients with advanced hepatocellular carcinoma (HCC) till now is sorafenib. A preliminary study suggested that capecitabine, an oral fluoropyrimidine, may be effective in advanced HCC. We have tested this hypothesis in this phase 2 study. In this single-center, phase 2, open-label trial, we randomly assigned 52 patients with advanced HCC who had not received previous systemic treatment to receive either sorafenib (at a dose of 400 mg twice daily) or capecitabine (1,000 mg/m(2) twice daily) (day 1-day 14). Primary outcome was progression-free survival. Secondary outcomes included the overall survival and safety. Median overall survival was 7.05 months in the sorafenib group and 5.07 months in the capecitabine group (hazard ratio in the capecitabine group 2.36; 95 % confidence interval 1.174-4.74; P < 0.016). The median progression-free survival was 6 months in the sorafenib group and 4 months in the capecitabine group (P < 0.005). Three patients in the sorafenib group (11.5 %) and one patient in the capecitabine group (3 %) had a partial response; one patient (3 %) had a complete response in the sorafenib group. Hand-foot skin reaction was more frequent in the sorafenib group, hyperbilirubinemia was more common in the capecitabine group, and diarrhea was equivalent between both groups. In patients with advanced HCC, capecitabine is inferior to sorafenib in terms of median progression-free survival and overall survival, and it should not be used alone for the treatment of advanced HCC, but rather, combination therapy with sorafenib should be considered.
In limited resource countries like Egypt, we suggest that the use of sorafenib for the treatment of advanced HCC cases should be restricted to a highly selected subgroup of patients with good performance and child A.
In this paper a new fast facial recognition system employing the principal component analysis, in the transform domain, and in conjunction with vector quantization, TD2DPCA/VQ, is presented. A transform domain two dimensional principal component analysis algorithm (TD2DPCA) was recently reported which possesses high recognition accuracy and low storage and computational requirements. The TD2DPCA/VQ presented here, maintains the recognition accuracy of the TD2DPCA while considerably improving the storage and computational properties. Employing the TD2DPCA/VQ, the storage and computational requirements are reduced by a factor P, where P is the number of training images (poses) per individual, used in the training mode.Experimental results employing the ORL and Yale databases confirm these excellent properties, where it is shown that the storage requirements and the computational complexity, for P=5, are reduced by 80% compared to the, high-performance, TD2DPCA algorithm .
In low-middle income countries (LMICs) and the Middle East and North Africa (MENA) region, there is an unmet need to establish and improve breast cancer (BC) awareness, early diagnosis and risk reduction programs. During the 12th Breast, Gynecological & Immuno-oncology International Cancer Conference -Egypt 2020, 26 experts from 7 countries worldwide voted to establish the first consensus for BC awareness, early detection and risk reduction in LMICs/MENA region. The panel advised that there is an
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